Opinion Mapping Travelblogs

Efthymios Drymonas Alexandros Efentakis

Dieter PfoserResearch Center Athena

Institute for the Management of Information SystemsAthens, Greece

http://www.imis.athena-innovation.gr

Users create vast amounts of “geospatial” narratives

…travel diaries, travel blogs…How to quickly assess them?

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Introduction

• Simple assessment of user-generated geospatial content

• Visualization • Geospatial opinion maps

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Motivation

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Opinion Mapping generating steps

1. Relating text to location – Geocoding

2. Relating user sentiment to text – Opinion Coding

3. Relating opinions to location – Opinion Mapping

1. Relating text to location – Geocoding

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a) Web crawlingb) Geoparsingc) Geocoding

1a. Web Crawling• Crawled for travel blog articles• Parsed ~ 150k HTML documents

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1b. Geoparsing -Processing Pipeline Overview

• GATE• Cafetiere IE system• YAHOO! API– Placemaker– Placefinder

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1b. Linguistic Preprocessing

• Tokeniser & Orthographic Analyser • Sentence Splitter • POS Tagger • Morphological Analysis, WordNet

– Ex. “went south”, “goes south” = “go south”

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1b. Semantic Analysis: i. Ontology Lookup

Ontology access to retrieve potential semantic class information

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1b. Semantic Analysis: ii. Feature Extraction (IE engine)

• Compilation of semantic analysis rules• IE engine uses all previous info– Linguistic information (POS tags,

orthographic info etc.)– Semantic and context information

• Extraction of spatial objects10

1c. PostProcessor - Geocoding

• Collecting semantic analysis results and annotating them to the original text

• Preparing the input to the geocoder module

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1c. Geocoding

• Place name info from semantic analysis transformed to coordinates

• YAHOO! Placemaker for disambiguation • YAHOO! Placefinder geocoder

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output XML file• From plain text

to structured information

• Also global document info extracted

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2. Relating user sentiment to text–

Opinion Coding 1/2• OpinionFinder tool• Annotates text with positive or negative

sentiments• Retain paragraphs only containing spatial info• Total positive and negative sentiments for

each paragraph 14

2. Relating user sentiment to text–

Opinion Coding 2/2

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• Score for this paragraph : +2

3. Mapping opinions to location -Opinion Mapping

Scoring methodSpatial grid

Aggregation method

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Opinion Mapping (Scoring)• Each paragraph is characterized by a MBR

– Visualized paragraph’s MBR do not exceed 0.5º x 0.5º

• Each paragraph’s MBR is mapped to a sentiment color according to users’ opinions

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Opinion Mapping (Issues)

Problem: • Multiple paragraphs may partially target

the same area (overlapping areas)• How to visualize partially overlapping

MBRs of different paragraphs and sentiments

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Opinion Mapping (Spatial grid)

Solution:• We split earth into small tiles of

0.0045º x 0.0045º (~500m x 500m)• Each paragraph’s MBR consists of

several such small tiles

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Opinion Mapping (Aggregation Method) 1/2

• Partially overlapping paragraph MBRs translated to a set of overlapping tiles– Sentiment aggregation per tile (for

drawing purposes)• Instead of sentiment aggregation per MBR

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Opinion Mapping (Aggregation Method) 2/2

An example:• For one cell/tile there are four

scores: -1, -2, 1, 0

• Resulting score is their sum: -221

Opinion Mapping examples

22Original MBRs of paragraphs

Opinion Mapping examples

23Paragraph MBRs divided in tiles – Aggregation per tile

Opinion Mapping examples

24Final result

Conclusions• Aggregating opinions is important for utilizing and

assessing user-generated content• Total of more than 150k web pages/articles were

processed• Sentiment information from various articles is

aggregated and visualized• Relate portions of texts to locations• Geospatial opinion-map based on user-contributed

information

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Future Work

• Better approach on sentiment analysis• More in-depth analysis of the results• Examine micro blogging content streams• Live updated sentiment information

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End.. Questions?

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